Sohaib Abbasi@sohaib_ahmed_abbasi181

Business Case: AI-Powered Product Recommendation System The AI-powered product recommendation system is designed to enhance customer engagement and increase conversion rates on retail websites by integrating with chatbot interfaces. This solution addresses key business challenges by improving lead generation and boosting click-through rates (CTR). Lead Generation: Traditional chatbots on retail websites are often limited to basic customer support or navigation assistance. However, they lack the ability to actively guide users toward relevant products, resulting in missed opportunities to generate leads. Our AI-enhanced chatbot leverages natural language processing (NLP) to understand user queries and recommend personalized products based on the input. This allows the chatbot to engage users in a more meaningful way, converting passive browsing into active lead generation. By offering tailored product suggestions, users are more likely to become qualified leads, improving the chances of eventual conversion. Improved CTR: With traditional search filters, users often have to sift through many irrelevant products. Our system helps improve click-through rates by presenting only the most relevant products based on the user's description. By reducing the time spent searching and increasing the relevance of the results, users are more likely to click on the products suggested, driving higher engagement and interaction rates. In summary, the AI-powered recommendation system not only provides a more personalized shopping experience but also actively contributes to increasing lead generation and improving CTR on retail websites.

MeetAssist addresses common challenges associated with meetings, such as unclear goals, time wastage, and confusion about next steps. Research shows that a significant portion of employees’ time is spent in meetings and unproductive ones result in billions of dollars in annual losses. To address these problems, the solution is split into two phases: Pre-Meeting and Post-Meeting. In the Pre-Meeting phase, MeetAssist helps users create detailed, goal-oriented agendas, ensuring every meeting starts with a well-defined purpose and clear objectives. The Post-Meeting phase focuses on analyzing meeting transcripts to provide actionable insights. This includes summarizing key points, extracting action items, assessing participant sentiments, offering productivity analysis, and suggesting improvements to enhance future meetings. MeetAssist aims to maximize the value derived from every meeting, benefiting both teams and individuals. It leverages the Llama 3 70B Instruct generative AI model from the IBM Watsonx platform for its core features. The tech stack used for it inludes Python with Flask for backend development, HTML, CSS, Bootstrap, and JavaScript for the frontend, Docker for containerization, and Render for hosting.